The present invention relates to a system for automatically measuring, recording and predicting interfloor traffic for group control of elevator cars.
It is very important to accurately measure interfloor passenger traffic so as to provide basic research data for determining an operation system of the elevator cars in order to ensure effective utilization thereof. In group control of elevator cars, traffic demand for the elevator system has a certain degree of regularity in accordance with seasonal, daily, hourly and weather factors. By utilizing these factors, the elevator cars can be efficiently operated. If a day is divided into time periods to monitor the interfloor traffic in each time period of every day, a degree of regularity will be found which may be effectively utilized to effectively operate the elevator cars.
In a conventional elevator system, a researcher monitors floors at which a car stops, the number of incoming and outgoing passengers, and the time when the car stops, while he remains in the elevator car. However, it is very difficult to monitor the number of passengers with respect to each of origin (starting)- destination (stopping) floor pairs. Another type of conventional research has been conducted wherein research slips are handed to passengers at their origin floors and are collected at their destination or arrival floors so as to monitor the number of passengers with respect to each of origin-destination floor pairs. However, with this research method, a large number of researchers is required as well as the cooperation of the passengers. For this reason, this research cannot be conducted over a long period of time. Furthermore, the behavior of individual passengers is directly observed creating problems from the viewpoint of privacy. It is also possible that passengers will behave in an unusual manner since they are overconscious of the research. For these reasons, it is very difficult to accurately monitor passenger traffic with respect to each of origin-desination floor pairs.
In the case of a group control for a group of elevators, it is important to determine which elevator car should respond to a given hall or landing call registered on a service floor in order to operate efficiently. For example, if an elevator car which can first answer a landing call is simply assigned thereto, the elevator car cannot then answer any other landing call, since the car must transfer a passenger or passengers who wait on the service floors. Therefore, in the case of answering any hall call, it is very important to accurately predict a new car call or derived (secondary) call which will be registered by the new passenger in the car.
If the derived call can be accurately predicted, car calls registered in elevator cars which can answer the hall call will be compared with the derived call predicted from the hall call so as to assign the most suitable elevator car to the hall call. As a result, the present car call and the derived call can be responded most efficiently. Namely, the number of stops of elevator cars can be decreased to enhance the service capability of the elevator system. Furthermore, since the most suitable elevator car is assigned to each hall call, an average time (average response time) taken for an elevator car to respond to the hall call can be shortened. As will be apparent from the foregoing, if derived calls to be made in the elevator car can be properly predicted, then a very efficient group control of elevator cars will be efficiently performed.
However, the proper prediction of the derived calls is very difficult to perform. Various studies have been made to solve this problem. For example, according to a prediction method, hall calls registered during the previous elevator operation are recorded together with secondary calls derived from the hall calls to predict secondary calls which will be made during the present elevator operation. This prediction method is very effective when traffic demand is substantially constant. However, when the traffic demand greatly varies according to hours of a day as in office buildings, the prediction accuracy is greatly degraded. Therefore, this prediction method cannot be effectively utilized. Furthermore, the traffic cannot be accurately measured simply by recording the derived calls. As a result, traffic demand cannot be properly predicted.